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The initialization of initial generating equipment maintenance schedules while a heuristic method is used
Author(s) -
Pavel Y. Gubin,
Губин Павел Юрьевич,
Vladislav Oboskalov,
Обоскалов Владислав Петрович
Publication year - 2021
Publication title -
vestnik samarskogo gosudarstvennogo tehničeskogo universiteta. seriâ: tehničeskie nauki/vestnik samarskogo gosudarstvennogo tehničeskogo universiteta. seriâ, tehničeskie nauki
Language(s) - English
Resource type - Journals
eISSN - 2712-8938
pISSN - 1991-8542
DOI - 10.14498/tech.2021.2.1
Subject(s) - initialization , computer science , mathematical optimization , schedule , heuristic , scheduling (production processes) , probabilistic logic , differential evolution , tardiness , algorithm , job shop scheduling , mathematics , artificial intelligence , programming language , operating system
Currently, heuristic methods based on iterative changing of feasible solutions set provide a perspective tool for generation equipment maintenance scheduling in power systems. Wherein effectiveness of a heuristic method depends significantly on the initial set of possible schedules or in other words quality of the method initialization. In this case, a widely used methodology of building the initial array of solutions on the basis of pseudorandom uniform generation of control variables seems to be only palliative way to access the problem. This paper proposes alternative initialization procedure drawing on the example of generating units maintenance planning with heuristic differential evolution method. The principle of this method is to get initial set of solutions utilizing normal probability distribution to generate pseudorandom deviations from the suboptimal maintenance schedule which is to be preliminarily formed using directed search method. Following this approach allows to improve probabilistic characteristics of resultant maintenance schedule in particular to decrease median value of an objective function and its coefficient of variation, and to maximize probability to get the combination of units outage moments completely suiting operational constraints.

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